When analyzing data in the presence of over dispersion, the usual practice is to assume a common dispersion parameter to all observations. However, there are situations where the assumption of homogeneity of the dispersion parameter does not hold.
Morales Mario Alfonso +1 more
doaj
Clinical Trial Design Using A Stopped Negative Binomial Distribution. [PDF]
DeVeaux M, Kane MJ, Zelterman D.
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Measured PET Data Characterization with the Negative Binomial Distribution Model. [PDF]
Santarelli MF, Positano V, Landini L.
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Confidence Intervals for Asbestos Fiber Counts: Approximate Negative Binomial Distribution. [PDF]
Bartley D, Slaven J, Harper M.
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Linking parasite populations in hosts to parasite populations in space through Taylor's law and the negative binomial distribution. [PDF]
Cohen JE, Poulin R, Lagrue C.
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Weighted negative binomial distribution: properties and applications. [PDF]
Satheesh Kumar C, Sathyan P.
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Characterization of the Bivariate Negative Binomial Distribution [PDF]
Dunn, James E.
core +2 more sources
Mathematical Theory of Seismic Activity and Its Specific Cases: Gutenberg-Richter Law, Omori Law, Roll-Off Effect, and Negative Binomial Distribution. [PDF]
Borisov R, Vitanov NK.
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From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq. [PDF]
Wang Y, Shu Z, Cao Z, Grima R.
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Bounds for survival probabilities in supercritical Galton-Watson processes and applications to population genetics. [PDF]
Bürger R.
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